Rapid Analysis of Ash Composition Using Laser-Induced Breakdown Spectroscopy (LIBS)

Abstract

Inorganic compounds are known to be problematic in the thermochemical conversion of biomass to syngas and ultimately hydrocarbon fuels. The elements Si, K, Ca, Na, S, P, Cl, Mg, Fe, and Al are particularly problematic and are known to influence reaction pathways, contribute to fouling and corrosion, poison catalysts, and impact waste streams. Substantial quantities of inorganic species can be entrained in the bark of trees during harvest operations. Herbaceous feedstocks often have even greater quantities of inorganic constituents, which can account for as much as one-fifth of the total dry matter.Current methodologies to measure the concentrations of these elements, such as inductively coupled plasma-optical emission spectrometry/mass spectrometry (ICP-OES/MS) are expensive in time and reagents. This study demonstrates that a new methodology employing laser-induced breakdown spectroscopy (LIBS) can rapidly and accurately analyze the inorganic constituents in a wide range of biomass materials, including both woody and herbaceous examples. This technique requires little or no sample preparation, does not consume any reagents, and the analytical data is available immediately. In addition to comparing LIBS data with the results from ICP-OES methods, this work also includes discussions of sample preparation techniques, calibration curves for interpreting LIBS spectra, minimum detection limits, and the use of internal standards and standard reference materials. SUMMARYThe results from the LIBS calibrations for Al, Ca, Fe, Mg, Mn, P, K, Na and Si is presented in Table 1 and Figures 8 through 11 and indicate that LIBS is promising as a rapid screening technique with an accuracy comparable to that of the acid digestion methods for all of the elements. Importantly, ICP-OES/MS methods, which involve acid digestions, have benefited from many years of widespread research and repeated use, while the LIBS approach is yet emerging for measuring inorganic constituents in biomass. Accordingly, significant improvement is expected in the LIBS approach. For example, the LIBS calibrations in this work employ univariate curve fits, which are usually quite poor compared multivariate calibrations, although the simplicity of univariate methods makes them attractive. A further consideration is that the univariate LIBS calibrations exhibit a high degree of correlation, considering uncertainties in calibration data, and it is possible that multivariate models would only improve the fits by modeling experimental noise, which could make the fits less reliable for new samples. Multivariate models do have advantages, though, in being able to account for interaction effects between elements of interest. It is demonstrated in Fig. 12 that LIBS is useful for measuring total ash content minus, except Cl and S. Further refinement to the LIBS technique, such as a more sensitive detector or the use of an argon purge gas should make it possible to reliably analyze Cl, S and possibly O, N, and H.It is observed that the LIBS calibration curves f...

Analytical Methods for the Rapid Determination of Solid Biofuel Quality

“…In most cases, the initial cost of these instruments for biomass CHP plants is quite high. Currently, the most promising methods to determine the chemical composition of solid biofuels are XRF 141, 173, 175, 176, 178–183 and LIBS 177, 204–210. Various devices have already been evaluated in recent studies.…”

“…This method has already been researched several times for analysing different parameters like A, Q, and trace elements of coal, ashes and biomass [79,136,175,176,[195][196][197][198][199][200][201][202][203]. Various studies show the value of LIBS systems for rapid measurement of defined fuel properties of solid biofuels, excluding M [177,[204][205][206][207][208][209][210]. Another suitable laser-based method for the rapid analysis of solid biofuels is the laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS).…”

“…Ash content [207,208,210] [ 79,197] Calorific value [207,208,210] [ 79,197,202,203] Elemental content [177,[204][205][206][207][208][209]…”